The overall mission of the proposed University of Michigan NIEHS Center is to promote new translational research using novel multi-disciplinary approaches to better understand the impact of environmental exposures on risk of selected adult chronic diseases through mechanisms involving epigenetic modifications during vulnerable stages of life. As the range of technologies and high-throughput methods available to address this challenge expands, the importance of having access to advanced bioinformatics support is growing. The goal of the Bioinformatics Core (BIC) is to enhance the interpretation of experimental and clinical results from a broad range of epigenetic studies. The objective is to provide advanced-level support and assistance in bioinformatics analyses of studies involving University of Michigan NIEHS P30 Center investigators. Please note that italicized text is used throughout to mark notable updates and revision(s). Including excerpts of critiques and our associated responses. Environmental exposures may increase the risk of adult chronic disease through any of several complex mechanisms involving DNA methylation, histone modifications, nucleosome rearrangement, DNA mutations or instability, alternative splicing, alteration of metabolite levels, or other pathway dysregulation leading to altered protein levels or function. We predict some investigators will be measuring one or more of these markers on a genome-wide scale, requiring bioinformatics analysis not familiar to investigators in the Department of Environmental Health Sciences or other labs performing such experiments. However, even when studying one or a small set of markers, identifying and interpreting the relevant interactions and downstream effects of such changes often benefits from sophisticated bioinformatics analysis. The BIC will support the members of the proposed U of M NIEHS Center to identify and articulate their bioinformatics needs, and will provide the support required to successfully address these challenges, making use of a wealth of publicly available bioinformatics tools and novel custom analyses. We note here that the splitting out of the Bioinformatics Core as a unit separate from the Environmental Statistics Core (ESC) is, in part, a response to the critique of our first application In which the study section noted that The bioinformatics support is insufficient to support the Center's needs. In addition, it is a reflection of the distinct nature of bioinformatics as a discipline as well as the rapid growth and maturation of our Bioinformatics resources, making the creation of this new Core a major potential strength of our Center Nevertheless, It Is also important to emphasize that our Bioinformatics Core will retain strong connections to the Environmental Statistics Core in terms of common interests in statistical methodology, curability to provide annotations and biological Interpretation to the analyses ofthe ESC, the connecting role ofDr Sartor, as she has joint appointments in both CCMB and the Department of Biostatistics, and the several collaborative projects proposed between the two Cores (monthly seminar series, joint workshop, data tutorial days). We have also created an operational plan ensuring optimal coordination of our activities in terms of informing Center members of our individual capabilities, providing coordination of our translational services (see the Integrated Health Sciences Core), and assisting each other with methodological development and research. As with the ESC, it is important to note that the BIC does not have the responsibility of managing and tracking the complex flow of data that will be generated by the Facilities Cores and individual Center members. The P30 has a rigorous, structured plan for that activity through the efforts of a unit housed in the Integrated Health Sciences Core (see Section 7.3). The core faculty of the BIC have been selected to reflect our expertise in analysis of genome-level data as measured through DNA microarray and high-throughput sequencing applications, in exploring interaction networks and biological pathways, and in proteomics and metabolomics.